Combining Bagging, Boosting and Random Subspace Ensembles for Regression Problems
نویسندگان
چکیده
Bagging, boosting and random subspace methods are well known re-sampling ensemble methods that generate and combine a diversity of learners using the same learning algorithm for the base-regressor. In this work, we built an ensemble of bagging, boosting and random subspace methods ensembles with 8 sub-regressors in each one and then an averaging methodology is used for the final prediction. We performed a comparison with simple bagging, boosting and random subspace methods ensembles with 25 sub-regressors, as well as other well known combining methods, on standard benchmark datasets and the proposed technique had better correlation coefficient in most cases.
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